Cardiovascular disease (CVD) accounts for one in every two deaths in the United States and is the number one killer disease in the United States and most European countries. Thus, prevention of cardiovascular disease is an area of major public health importance. A low-fat diet and exercise are recommended to prevent CVD. In addition, a number of therapeutic agents may be prescribed by medical professionals to those individuals who are known to be at risk having CVD. More aggressive therapy, such as administration of multiple medications or surgical intervention may be used in those individuals who are at high risk of having CVD. Since CVD therapies may have adverse side effects, it is desirable to have methods for identifying those individuals who are at risk, particularly those individuals who are at high risk of experiencing an adverse cardiovascular event near term.
Currently, several risk factors are used by medical professionals to assess an individual's risk of developing or having CVD and to identify individuals at high risk. Major risk factors for cardiovascular disease include age, hypertension, family history of premature CVD, smoking, high total cholesterol, high LDL cholesterol, low HDL cholesterol, obesity and diabetes. The major risk factors for CVD are additive, and are typically used together by physicians in a risk prediction algorithm to target those individuals who are most likely to benefit from treatment for CVD. These algorithms achieve a high sensitivity and specificity for predicting risk of CVD within 10 years. However, the ability of the present algorithms to predict a higher probability of developing CVD is limited. Among apparently healthy adults with none of the current risk factors, the 10-year risk for developing CVD is still considerable, and is approximately 2% or higher, depending upon age. In addition, a large number of CVD complications occur in individuals with apparently low to moderate risk profiles, as determined using currently known risk factors. Thus, there is a need to expand the present cardiovascular risk algorithm to identify a larger spectrum of individuals at risk for or affected with CVD.
A significant factor in the development of cardiovascular disease is the presence of atherosclerosis. However, the mechanism of atherosclerosis is not well understood. Over the past decade a wealth of clinical, pathological, biochemical and genetic data support the notion that atherosclerosis is a chronic inflammatory disorder. Acute phase reactants (e.g. C-reactive protein, complement proteins), sensitive but non-specific markers of inflammation, are enriched in fatty streaks and later stages of atherosclerotic lesions. In a recent prospective clinical trial, base-line plasma levels of C-reactive protein independently predicted risk of first-time myocardial infarction and stroke in apparently healthy individuals. U.S. Pat. No. 6,040,147 describes methods which use C-reactive protein, cytokines, and cellular adhesion molecules to characterize an individual's risk of developing a cardiovascular disorder. Although useful, these markers may be found in the blood of individuals with inflammation due to causes other than CVD, and thus, these markers may not be specific enough. Moreover, modulation of their levels has not been established to reproducibly predict a decrease in the morbidity or mortality of CVD. Accordingly, there exists a need for additional markers for assessing a subject's risk of CVD.